In the vast and expanding ocean of digital content, users are hardly satisfied with recommended programs solely based on static user patterns and common statistics. Therefore, there is growing interest in recommendation approaches that aim to provide a certain level of diversity, besides precision and ranking. Context-awareness, which is an effective way to express dynamics and adaptivity, is widely used in recommender systems to set a proper balance between ranking and diversity.In light of these observations, we introduce a recommender with a context-aware probabilistic graphical model and apply it to a campus-wide TV content delivery system named "Vision". Within this recommender, selection criteria of candidate fields and contextual factors are designed and users' dependencies on their personal preference or the aforementioned contextual influences can be distinguished. Most importantly, as to the role of balancing relevance and diversity, final experiment results prove that context-aware LDA can evidently outperform other algorithms on both metrics. Thus this scalable model can be flexibly used for different recommendation purposes.